





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput

def 				SCREAMING_SNAKE_CASE__							(	snake_case__	:Optional[Any]      ,				snake_case__	:Optional[Any]=0.999      ,				snake_case__	:Tuple="cosine"      ,				)   ->    Optional[Any]:
  if alpha_transform_type == "cosine":

    def alpha_bar_fn(snake_case__	:List[Any]  ):
      return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(snake_case__	:Optional[int]  ):
      return math.exp(t * -12.0  )

  else:
    raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""  )

  _lowercase						    =			[]
  for i in range(snake_case__  ):
    _lowercase						    =			i / num_diffusion_timesteps
    _lowercase						    =			(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(snake_case__  ) / alpha_bar_fn(snake_case__  )      ,				snake_case__  )  )
  return torch.tensor(snake_case__      ,				dtype=torch.floataa  )







class 					A_					(   UpperCAmelCase       ,						UpperCAmelCase						):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_   :			Optional[Any]  			=	[e.name for e in KarrasDiffusionSchedulers]
       SCREAMING_SNAKE_CASE_   :			Dict  			=	2







       @register_to_config
       def __init__(     self						:						Union[str, Any]    ,__A						:						int = 1000    ,__A						:						float = 0.00085    ,__A						:						float = 0.012    ,__A						:						str = "linear"    ,__A						:						Optional[Union[np.ndarray, List[float]]] = None    ,__A						:						str = "epsilon"    ,__A						:						str = "linspace"    ,__A						:						int = 0    ,)    ->       Union[str, Any]:
         if trained_betas is not None:
           _lowercase						    =			torch.tensor(__A    ,dtype=torch.floataa	)
         elif beta_schedule == "linear":
           _lowercase						    =			torch.linspace(__A    ,__A    ,__A    ,dtype=torch.floataa	)
         elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           _lowercase						    =			(
               torch.linspace(beta_start**0.5    ,beta_end**0.5    ,__A    ,dtype=torch.floataa	) ** 2
           )
         elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _lowercase						    =			betas_for_alpha_bar(__A	)
         else:
           raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""	)

         _lowercase						    =			1.0 - self.betas
         _lowercase						    =			torch.cumprod(self.alphas    ,dim=0	)

         #  set all values
         self.set_timesteps(__A    ,__A    ,__A	)







       def  __UpperCAmelCase			(     self						:						str    ,__A						:						List[str]    ,__A						:						Any=None	)    ->       str:
         if schedule_timesteps is None:
           _lowercase						    =			self.timesteps

         _lowercase						    =			(schedule_timesteps == timestep).nonzero()

         # The sigma index that is taken for the **very** first `step`
         # is always the second index (or the last index if there is only 1)
         # This way we can ensure we don't accidentally skip a sigma in
         # case we start in the middle of the denoising schedule (e.g. for image-to-image)
         if len(self._index_counter	) == 0:
           _lowercase						    =			1 if len(__A	) > 1 else 0
         else:
           _lowercase						    =			timestep.cpu().item() if torch.is_tensor(__A	) else timestep
           _lowercase						    =			self._index_counter[timestep_int]

         return indices[pos].item()







       @property
       def  __UpperCAmelCase			(     self						:						int	)    ->       Tuple:
         # standard deviation of the initial noise distribution
         if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

         return (self.sigmas.max() ** 2 + 1) ** 0.5







       def  __UpperCAmelCase			(     self						:						List[str]    ,__A						:						torch.FloatTensor    ,__A						:						Union[float, torch.FloatTensor]    ,)    ->       torch.FloatTensor:
         _lowercase						    =			self.index_for_timestep(__A	)

         if self.state_in_first_order:
           _lowercase						    =			self.sigmas[step_index]
         else:
           _lowercase						    =			self.sigmas_interpol[step_index]

         _lowercase						    =			sample / ((sigma**2 + 1) ** 0.5)
         return sample







       def  __UpperCAmelCase			(     self						:						Tuple    ,__A						:						int    ,__A						:						Union[str, torch.device] = None    ,__A						:						Optional[int] = None    ,)    ->       str:
         _lowercase						    =			num_inference_steps

         _lowercase						    =			num_train_timesteps or self.config.num_train_timesteps

         # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
         if self.config.timestep_spacing == "linspace":
           _lowercase						    =			np.linspace(0    ,num_train_timesteps - 1    ,__A    ,dtype=__A	)[::-1].copy()
         elif self.config.timestep_spacing == "leading":
           _lowercase						    =			num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           _lowercase						    =			(np.arange(0    ,__A	) * step_ratio).round()[::-1].copy().astype(__A	)
           timesteps += self.config.steps_offset
         elif self.config.timestep_spacing == "trailing":
           _lowercase						    =			num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           _lowercase						    =			(np.arange(__A    ,0    ,-step_ratio	)).round().copy().astype(__A	)
           timesteps -= 1
         else:
           raise ValueError(
               F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

         _lowercase						    =			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
         _lowercase						    =			torch.from_numpy(np.log(__A	)	).to(__A	)

         _lowercase						    =			np.interp(__A    ,np.arange(0    ,len(__A	)	)    ,__A	)
         _lowercase						    =			np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
         _lowercase						    =			torch.from_numpy(__A	).to(device=__A	)

         # interpolate sigmas
         _lowercase						    =			sigmas.log().lerp(sigmas.roll(1	).log()    ,0.5	).exp()

         _lowercase						    =			torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
         _lowercase						    =			torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)

         if str(__A	).startswith('mps'	):
           # mps does not support float64
           _lowercase						    =			torch.from_numpy(__A	).to(__A    ,dtype=torch.floataa	)
         else:
           _lowercase						    =			torch.from_numpy(__A	).to(__A	)

         # interpolate timesteps
         _lowercase						    =			self.sigma_to_t(__A	).to(__A    ,dtype=timesteps.dtype	)
         _lowercase						    =			torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])    ,dim=-1	).flatten()

         _lowercase						    =			torch.cat([timesteps[:1], interleaved_timesteps]	)

         _lowercase						    =			None

         # for exp beta schedules, such as the one for `pipeline_shap_e.py`
         # we need an index counter
         _lowercase						    =			defaultdict(__A	)







       def  __UpperCAmelCase			(     self						:						Optional[Any]    ,__A						:						str	)    ->       List[str]:
         # get log sigma
         _lowercase						    =			sigma.log()

         # get distribution
         _lowercase						    =			log_sigma - self.log_sigmas[:, None]

         # get sigmas range
         _lowercase						    =			dists.ge(0	).cumsum(dim=0	).argmax(dim=0	).clamp(max=self.log_sigmas.shape[0] - 2	)
         _lowercase						    =			low_idx + 1

         _lowercase						    =			self.log_sigmas[low_idx]
         _lowercase						    =			self.log_sigmas[high_idx]

         # interpolate sigmas
         _lowercase						    =			(low - log_sigma) / (low - high)
         _lowercase						    =			w.clamp(0    ,1	)

         # transform interpolation to time range
         _lowercase						    =			(1 - w) * low_idx + w * high_idx
         _lowercase						    =			t.view(sigma.shape	)
         return t







       @property
       def  __UpperCAmelCase			(     self						:						Any	)    ->       Union[str, Any]:
         return self.sample is None







       def  __UpperCAmelCase			(     self						:						Dict    ,__A						:						Union[torch.FloatTensor, np.ndarray]    ,__A						:						Union[float, torch.FloatTensor]    ,__A						:						Union[torch.FloatTensor, np.ndarray]    ,__A						:						bool = True    ,)    ->       Union[SchedulerOutput, Tuple]:
         _lowercase						    =			self.index_for_timestep(__A	)

         # advance index counter by 1
         _lowercase						    =			timestep.cpu().item() if torch.is_tensor(__A	) else timestep
         self._index_counter[timestep_int] += 1

         if self.state_in_first_order:
           _lowercase						    =			self.sigmas[step_index]
           _lowercase						    =			self.sigmas_interpol[step_index + 1]
           _lowercase						    =			self.sigmas[step_index + 1]
         else:
           # 2nd order / KDPM2's method
           _lowercase						    =			self.sigmas[step_index - 1]
           _lowercase						    =			self.sigmas_interpol[step_index]
           _lowercase						    =			self.sigmas[step_index]

         # currently only gamma=0 is supported. This usually works best anyways.
         # We can support gamma in the future but then need to scale the timestep before
         # passing it to the model which requires a change in API
         _lowercase						    =			0
         _lowercase						    =			sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
           _lowercase						    =			sigma_hat if self.state_in_first_order else sigma_interpol
           _lowercase						    =			sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
           _lowercase						    =			sigma_hat if self.state_in_first_order else sigma_interpol
           _lowercase						    =			model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
         elif self.config.prediction_type == "sample":
           raise NotImplementedError('prediction_type not implemented yet: sample'	)
         else:
           raise ValueError(
               F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""	)

         if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           _lowercase						    =			(sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           _lowercase						    =			sigma_interpol - sigma_hat

           # store for 2nd order step
           _lowercase						    =			sample
         else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           _lowercase						    =			(sample - pred_original_sample) / sigma_interpol

           # 3. delta timestep
           _lowercase						    =			sigma_next - sigma_hat

           _lowercase						    =			self.sample
           _lowercase						    =			None

         _lowercase						    =			sample + derivative * dt

         if not return_dict:
           return (prev_sample,)

         return SchedulerOutput(prev_sample=__A	)







       def  __UpperCAmelCase			(     self						:						List[Any]    ,__A						:						torch.FloatTensor    ,__A						:						torch.FloatTensor    ,__A						:						torch.FloatTensor    ,)    ->       torch.FloatTensor:
         # Make sure sigmas and timesteps have the same device and dtype as original_samples
         _lowercase						    =			self.sigmas.to(device=original_samples.device    ,dtype=original_samples.dtype	)
         if original_samples.device.type == "mps" and torch.is_floating_point(__A	):
           # mps does not support float64
           _lowercase						    =			self.timesteps.to(original_samples.device    ,dtype=torch.floataa	)
           _lowercase						    =			timesteps.to(original_samples.device    ,dtype=torch.floataa	)
         else:
           _lowercase						    =			self.timesteps.to(original_samples.device	)
           _lowercase						    =			timesteps.to(original_samples.device	)

         _lowercase						    =			[self.index_for_timestep(__A    ,__A	) for t in timesteps]

         _lowercase						    =			sigmas[step_indices].flatten()
         while len(sigma.shape	) < len(original_samples.shape	):
           _lowercase						    =			sigma.unsqueeze(-1	)

         _lowercase						    =			original_samples + noise * sigma
         return noisy_samples







       def __len__(     self						:						str	)    ->       Tuple:
         return self.config.num_train_timesteps

"""simple docstring"""



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


logging.set_verbosity_info()
a       :						Dict       		=		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
a       :						Dict       		=		[]
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.weight", F"encoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.self_attn.out_proj.bias", F"encoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.weight", F"encoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear1.bias", F"encoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.weight", F"encoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.linear2.bias", F"encoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.encoder.layers.{i}.norm1.weight", F"encoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.encoder.layers.{i}.norm1.bias", F"encoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.weight", F"encoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.encoder.layers.{i}.norm2.bias", F"encoder.layers.{i}.final_layer_norm.bias"))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.weight", F"decoder.layers.{i}.self_attn.out_proj.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.self_attn.out_proj.bias", F"decoder.layers.{i}.self_attn.out_proj.bias")
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.weight",
         F"decoder.layers.{i}.encoder_attn.out_proj.weight",
     )
 )
 rename_keys.append(
     (
         F"transformer.decoder.layers.{i}.multihead_attn.out_proj.bias",
         F"decoder.layers.{i}.encoder_attn.out_proj.bias",
     )
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.weight", F"decoder.layers.{i}.fc1.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear1.bias", F"decoder.layers.{i}.fc1.bias"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.weight", F"decoder.layers.{i}.fc2.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.linear2.bias", F"decoder.layers.{i}.fc2.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm1.weight", F"decoder.layers.{i}.self_attn_layer_norm.weight")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm1.bias", F"decoder.layers.{i}.self_attn_layer_norm.bias"))
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.weight", F"decoder.layers.{i}.encoder_attn_layer_norm.weight")
 )
 rename_keys.append(
     (F"transformer.decoder.layers.{i}.norm2.bias", F"decoder.layers.{i}.encoder_attn_layer_norm.bias")
 )
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.weight", F"decoder.layers.{i}.final_layer_norm.weight"))
 rename_keys.append((F"transformer.decoder.layers.{i}.norm3.bias", F"decoder.layers.{i}.final_layer_norm.bias"))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    _UpperCAmelCase        = state_dict.pop(_A   )
    _UpperCAmelCase        = val


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            _UpperCAmelCase        = key.replace("""backbone.0.body"""       ,   """backbone.conv_encoder.model"""   )
            _UpperCAmelCase        = value
        else:
            _UpperCAmelCase        = value

    return new_state_dict


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    _UpperCAmelCase        = """"""

    # first: transformer encoder
    for i in range(6   ):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6   ):
        # read in weights + bias of input projection layer of self-attention
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) to the state dict
        _UpperCAmelCase        = in_proj_weight[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias[:2_5_6]
        _UpperCAmelCase        = in_proj_weight[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias[-2_5_6:]
        # read in weights + bias of input projection layer of cross-attention
        _UpperCAmelCase        = state_dict.pop(
            F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""   )
        _UpperCAmelCase        = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""   )
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        _UpperCAmelCase        = in_proj_weight_cross_attn[:2_5_6, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[:2_5_6]
        _UpperCAmelCase        = in_proj_weight_cross_attn[2_5_6:5_1_2, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[2_5_6:5_1_2]
        _UpperCAmelCase        = in_proj_weight_cross_attn[-2_5_6:, :]
        _UpperCAmelCase        = in_proj_bias_cross_attn[-2_5_6:]


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Optional[int]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = image.size
    _UpperCAmelCase        = max(_A       ,   _A   )
    _UpperCAmelCase        = 8_0_0 if """detection""" in checkpoint_url else 1_0_0_0
    _UpperCAmelCase        = target_max_size / current_max_size
    _UpperCAmelCase        = image.resize((int(round(scale * width   )   ), int(round(scale * height   )   ))   )

    return resized_image


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = F.to_tensor(_A   )
    _UpperCAmelCase        = F.normalize(_A       ,   mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   )
    return image


@torch.no_grad()
def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	str:





    """simple docstring"""

    logger.info("""Converting model..."""   )

    # load original state dict
    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    # rename keys
    for src, dest in rename_keys:
        rename_key(_A       ,   _A       ,   _A   )
    _UpperCAmelCase        = rename_backbone_keys(_A   )
    # query, key and value matrices need special treatment
    read_in_q_k_v(_A   )
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _UpperCAmelCase        = """model."""
    for key in state_dict.copy().keys():
        if not key.startswith("""class_labels_classifier"""   ) and not key.startswith("""bbox_predictor"""   ):
            _UpperCAmelCase        = state_dict.pop(_A   )
            _UpperCAmelCase        = val
    # create HuggingFace model and load state dict
    _UpperCAmelCase        = TableTransformerConfig(
        backbone="""resnet18"""       ,   mask_loss_coefficient=1       ,   dice_loss_coefficient=1       ,   ce_loss_coefficient=1       ,   bbox_loss_coefficient=5       ,   giou_loss_coefficient=2       ,   eos_coefficient=0.4       ,   class_cost=1       ,   bbox_cost=5       ,   giou_cost=2       ,   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = 1_5
        _UpperCAmelCase        = 2
        _UpperCAmelCase        = {0: """table""", 1: """table rotated"""}
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}
    else:
        _UpperCAmelCase        = 1_2_5
        _UpperCAmelCase        = 6
        _UpperCAmelCase        = {
            0: """table""",
            1: """table column""",
            2: """table row""",
            3: """table column header""",
            4: """table projected row header""",
            5: """table spanning cell""",
        }
        _UpperCAmelCase        = idalabel
        _UpperCAmelCase        = {v: k for k, v in idalabel.items()}

    _UpperCAmelCase        = DetrImageProcessor(
        format="""coco_detection"""       ,   max_size=8_0_0 if """detection""" in checkpoint_url else 1_0_0_0   )
    _UpperCAmelCase        = TableTransformerForObjectDetection(_A   )
    model.load_state_dict(_A   )
    model.eval()

    # verify our conversion
    _UpperCAmelCase        = """example_pdf.png""" if """detection""" in checkpoint_url else """example_table.png"""
    _UpperCAmelCase        = hf_hub_download(repo_id="""nielsr/example-pdf"""       ,   repo_type="""dataset"""       ,   filename=_A   )
    _UpperCAmelCase        = Image.open(_A   ).convert("""RGB"""   )
    _UpperCAmelCase        = normalize(resize(_A       ,   _A   )   ).unsqueeze(0   )

    _UpperCAmelCase        = model(_A   )

    if "detection" in checkpoint_url:
        _UpperCAmelCase        = (1, 1_5, 3)
        _UpperCAmelCase        = torch.tensor(
            [[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]]   )

    else:
        _UpperCAmelCase        = (1, 1_2_5, 7)
        _UpperCAmelCase        = torch.tensor(
            [[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]]   )
        _UpperCAmelCase        = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]]   )

    assert outputs.logits.shape == expected_shape
    assert torch.allclose(outputs.logits[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    assert torch.allclose(outputs.pred_boxes[0, :3, :3]       ,   _A       ,   atol=1e-4   )
    print("""Looks ok!"""   )

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""   )
        Path(_A   ).mkdir(exist_ok=_A   )
        model.save_pretrained(_A   )
        image_processor.save_pretrained(_A   )

    if push_to_hub:
        # Push model to HF hub
        logger.info("""Pushing model to the hub..."""   )
        _UpperCAmelCase        = (
            """microsoft/table-transformer-detection"""
            if """detection""" in checkpoint_url
            else """microsoft/table-transformer-structure-recognition"""
        )
        model.push_to_hub(_A   )
        image_processor.push_to_hub(_A   )


if __name__ == "__main__":
 a       :						Dict       		=		argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
     type=str,
     choices=[
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
         '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
     ],
     help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 a       :						int       		=		parser.parse_args()
 convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)